Browsing by Author "Stoppel, Sergej"

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    Antarstick: Extracting Snow Height From Time-Lapse Photography
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Lang, Matěj; Mráz, Radoslav; Trtík, Marek; Stoppel, Sergej; Byška, Jan; Kozlikova, Barbora; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    The evolution and accumulation of snow cover are among the most important characteristics influencing Antarctica's climate and biotopes. The changes in Antarctica are also substantially impacting global climate change. Therefore, detailed monitoring of snow evolution is key to understanding such changes. One way to conduct this monitoring is by installing trail cameras in a particular region and then processing the captured information. This option is affordable, but has some drawbacks, such as the fully automatic solution for the extraction of snow height from these images is not feasible. Therefore, it still requires human intervention, manually correcting the inaccurately extracted information. In this paper, we present Antarstick, a tool for visual guidance of the user to potentially wrong values extracted from poor-quality images and support for their interactive correction. This tool allows for much quicker and semi-automated processing of snow height from time-lapse photography.
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    Honeycomb Plots: Visual Enhancements for Hexagonal Maps
    (The Eurographics Association, 2022) Trautner, Thomas; Sbardellati, Maximilian; Stoppel, Sergej; Bruckner, Stefan; Bender, Jan; Botsch, Mario; Keim, Daniel A.
    Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.
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    Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Trautner, Thomas; Bolte, Fabian; Stoppel, Sergej; Bruckner, Stefan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Scatter plots are a powerful and well-established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real-world data sets and validate its effectiveness in a user study.